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3 changes: 3 additions & 0 deletions Makefile
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version:
R -q -e "library('codecheck'); sessionInfo();"

install:
R -q -e "remotes::install_github('codecheckers/codecheck');"

render: version
R -q -e "codecheck::register_render()"

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73 changes: 11 additions & 62 deletions docs/certs/2020-001/index.html
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div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
.display.math{display: block; text-align: center; margin: 0.5rem auto;}
</style>
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Expand Down Expand Up @@ -230,15 +229,13 @@ <h1 class="title toc-ignore">CODECHECK Certificate 2020-001</h1>
<h4>
Previous
</h4>
</button>
<button type="button" onclick="changeImage(1)" class="btn btn-outline-secondary mx-2">
</button> <button type="button" onclick="changeImage(1)" class="btn btn-outline-secondary mx-2">
<h4>
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<!-- Cert image -->
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</div>
<div class="card-body d-flex flex-column">
<p>
<strong>Title</strong>: <a
href="https://doi.org/10.1093/gigascience/giaa026">ShinyLearner: A
containerized benchmarking tool for machine-learning classification of
tabular data.</a>
<strong>Title</strong>: <a href="https://doi.org/10.1093/gigascience/giaa026">ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data.</a>
</p>
<p>
<strong>Authors</strong>: Terry J Lee, Erica Suh, Kimball Hill, Stephen
R Piccolo
<strong>Authors</strong>: Terry J Lee, Erica Suh, Kimball Hill, Stephen R Piccolo
</p>
<!-- Abstract section -->
<div id="abstract-section" class="scrollable-container">
<p>
<strong>Abstract</strong>: <i>Obtained from <a
href="https://www.crossref.org">CrossRef</a></i>
<strong>Abstract</strong>: <i>Obtained from <a href="https://www.crossref.org">CrossRef</a></i>
</p>
<div id="abstract-content" class="scrollable-text-box">
<p>
<jats:title>Abstract</jats:title> <jats:sec>
<jats:title>Background</jats:title> <jats:p>Classification algorithms
assign observations to groups based on patterns in data. The
machine-learning community have developed myriad classification
algorithms, which are used in diverse life science research domains.
Algorithm choice can affect classification accuracy dramatically, so it
is crucial that researchers optimize the choice of which algorithm(s) to
apply in a given research domain on the basis of empirical evidence. In
benchmark studies, multiple algorithms are applied to multiple datasets,
and the researcher examines overall trends. In addition, the researcher
may evaluate multiple hyperparameter combinations for each algorithm and
use feature selection to reduce data dimensionality. Although software
implementations of classification algorithms are widely available,
robust benchmark comparisons are difficult to perform when researchers
wish to compare algorithms that span multiple software packages.
Programming interfaces, data formats, and evaluation procedures differ
across software packages; and dependency conflicts may arise during
installation.</jats:p> </jats:sec> <jats:sec>
<jats:title>Findings</jats:title> <jats:p>To address these challenges,
we created ShinyLearner, an open-source project for integrating
machine-learning packages into software containers. ShinyLearner
provides a uniform interface for performing classification, irrespective
of the library that implements each algorithm, thus facilitating
benchmark comparisons. In addition, ShinyLearner enables researchers to
optimize hyperparameters and select features via nested
cross-validation; it tracks all nested operations and generates output
files that make these steps transparent. ShinyLearner includes a Web
interface to help users more easily construct the commands necessary to
perform benchmark comparisons. ShinyLearner is freely available at <a
href="https://github.com/srp33/ShinyLearner"
class="uri">https://github.com/srp33/ShinyLearner</a>.</jats:p>
</jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>This
software is a resource to researchers who wish to benchmark multiple
classification or feature-selection algorithms on a given dataset. We
hope it will serve as example of combining the benefits of software
containerization with a user-friendly approach.</jats:p> </jats:sec>
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Classification algorithms assign observations to groups based on patterns in data. The machine-learning community have developed myriad classification algorithms, which are used in diverse life science research domains. Algorithm choice can affect classification accuracy dramatically, so it is crucial that researchers optimize the choice of which algorithm(s) to apply in a given research domain on the basis of empirical evidence. In benchmark studies, multiple algorithms are applied to multiple datasets, and the researcher examines overall trends. In addition, the researcher may evaluate multiple hyperparameter combinations for each algorithm and use feature selection to reduce data dimensionality. Although software implementations of classification algorithms are widely available, robust benchmark comparisons are difficult to perform when researchers wish to compare algorithms that span multiple software packages. Programming interfaces, data formats, and evaluation procedures differ across software packages; and dependency conflicts may arise during installation.</jats:p> </jats:sec> <jats:sec> <jats:title>Findings</jats:title> <jats:p>To address these challenges, we created ShinyLearner, an open-source project for integrating machine-learning packages into software containers. ShinyLearner provides a uniform interface for performing classification, irrespective of the library that implements each algorithm, thus facilitating benchmark comparisons. In addition, ShinyLearner enables researchers to optimize hyperparameters and select features via nested cross-validation; it tracks all nested operations and generates output files that make these steps transparent. ShinyLearner includes a Web interface to help users more easily construct the commands necessary to perform benchmark comparisons. ShinyLearner is freely available at <a href="https://github.com/srp33/ShinyLearner" class="uri">https://github.com/srp33/ShinyLearner</a>.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>This software is a resource to researchers who wish to benchmark multiple classification or feature-selection algorithms on a given dataset. We hope it will serve as example of combining the benefits of software containerization with a user-friendly approach.</jats:p> </jats:sec>
</p>
</div>
</div>
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<strong>Certificate identifier</strong>: 2020-001
</p>
<p>
<strong>Codechecker name</strong>: <a
href="https://orcid.org/0000-0001-8607-8025">Stephen J. Eglen</a>
<strong>Codechecker name</strong>: <a href="https://orcid.org/0000-0001-8607-8025">Stephen J. Eglen</a>
</p>
<p>
<strong>Time of codecheck</strong>: 2019-02-14 10:00:00
</p>
<p>
<strong>Repository</strong>: <a
href="https://github.com/codecheckers/Piccolo-2020"
class="uri">https://github.com/codecheckers/Piccolo-2020</a>
<strong>Repository</strong>: <a href="https://github.com/codecheckers/Piccolo-2020" class="uri">https://github.com/codecheckers/Piccolo-2020</a>
</p>
<p>
<strong>Codecheck report</strong>: <a
href="http://doi.org/10.5281/zenodo.3674056"
class="uri">http://doi.org/10.5281/zenodo.3674056</a>
<strong>Codecheck report</strong>: <a href="http://doi.org/10.5281/zenodo.3674056" class="uri">http://doi.org/10.5281/zenodo.3674056</a>
</p>
<!-- Summary -->
<div id="summary-section" class="scrollable-container">
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</p>
<div id="summary-content" class="scrollable-text-box">
<p>
Only visualiation steps performed, rather than machine learning (which
could take several hours/days). The created figures match those in the
article. The content of other output files was not checked.
Only visualiation steps performed, rather than machine learning (which could take several hours/days). The created figures match those in the article. The content of other output files was not checked.
</p>
</div>
</div>
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});

</script>
</div>

<p style="margin-bottom: 2em;">
</p>
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43 changes: 11 additions & 32 deletions docs/certs/2020-002/index.html
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div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
.display.math{display: block; text-align: center; margin: 0.5rem auto;}
</style>
</style>



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<h4>
Previous
</h4>
</button>
<button type="button" onclick="changeImage(1)" class="btn btn-outline-secondary mx-2">
</button> <button type="button" onclick="changeImage(1)" class="btn btn-outline-secondary mx-2">
<h4>
Next
</h4>
</button>
</div>
<!-- Cert image -->
<img class="card-img-top mb-3" src="cert_1.png" id="image-slider">
<!-- Cert image --> <img class="card-img-top mb-3" src="cert_1.png" id="image-slider">
</div>
</div>
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</div>
<div class="card-body d-flex flex-column">
<p>
<strong>Title</strong>: <a
href="https://doi.org/10.1088/0954-898X_3_1_008">The principal
components of natural images</a>
<strong>Title</strong>: <a href="https://doi.org/10.1088/0954-898X_3_1_008">The principal components of natural images</a>
</p>
<p>
<strong>Authors</strong>: Peter J. B. Hancock, Roland J. Baddeley,
Leslie S. Smith
<strong>Authors</strong>: Peter J. B. Hancock, Roland J. Baddeley, Leslie S. Smith
</p>
<!-- Abstract section -->
<div id="abstract-section" class="scrollable-container">
<p>
<strong>Abstract</strong>: <i>Obtained from <a
href="https://openalex.org">OpenAlex</a></i>
<strong>Abstract</strong>: <i>Obtained from <a href="https://openalex.org">OpenAlex</a></i>
</p>
<div id="abstract-content" class="scrollable-text-box">
<p>
AbstractA neural net was used to analyse samples of natural images and
text. For the natural images, components resemble derivatives of
Gaussian operators, similar to those found in visual cortex and inferred
from psychophysics. While the results from natural images do not depend
on scale, those from text images are highly scale dependent. Convolution
of one of the text components with an original image shows that it is
sensitive to inter-word gaps.
AbstractA neural net was used to analyse samples of natural images and text. For the natural images, components resemble derivatives of Gaussian operators, similar to those found in visual cortex and inferred from psychophysics. While the results from natural images do not depend on scale, those from text images are highly scale dependent. Convolution of one of the text components with an original image shows that it is sensitive to inter-word gaps.
</p>
</div>
</div>
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<strong>Certificate identifier</strong>: 2020-002
</p>
<p>
<strong>Codechecker names</strong>: <a
href="https://orcid.org/0000-0001-8607-8025">Stephen J. Eglen</a>, <a
href="https://orcid.org/0000-0002-0024-5046">Daniel Nüst</a>
<strong>Codechecker names</strong>: <a href="https://orcid.org/0000-0001-8607-8025">Stephen J. Eglen</a>, <a href="https://orcid.org/0000-0002-0024-5046">Daniel Nüst</a>
</p>
<p>
<strong>Time of codecheck</strong>: 2020-04-13 10:00:00
</p>
<p>
<strong>Repository</strong>: <a
href="https://github.com/codecheckers/Reproduction-Hancock"
class="uri">https://github.com/codecheckers/Reproduction-Hancock</a>
<strong>Repository</strong>: <a href="https://github.com/codecheckers/Reproduction-Hancock" class="uri">https://github.com/codecheckers/Reproduction-Hancock</a>
</p>
<p>
<strong>Codecheck report</strong>: <a
href="http://doi.org/10.5281/zenodo.3750741"
class="uri">http://doi.org/10.5281/zenodo.3750741</a>
<strong>Codecheck report</strong>: <a href="http://doi.org/10.5281/zenodo.3750741" class="uri">http://doi.org/10.5281/zenodo.3750741</a>
</p>
<!-- Summary -->
<div id="summary-section" class="scrollable-container">
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</p>
<div id="summary-content" class="scrollable-text-box">
<p>
Matlab code written by Iain Davies to reproduce original paper; natural
images provided by Peter Hancock.
Matlab code written by Iain Davies to reproduce original paper; natural images provided by Peter Hancock.
</p>
</div>
</div>
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});

</script>
</div>

<p style="margin-bottom: 2em;">
</p>
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div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
.display.math{display: block; text-align: center; margin: 0.5rem auto;}
</style>
</style>



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<h4>
Previous
</h4>
</button>
<button type="button" onclick="changeImage(1)" class="btn btn-outline-secondary mx-2">
</button> <button type="button" onclick="changeImage(1)" class="btn btn-outline-secondary mx-2">
<h4>
Next
</h4>
</button>
</div>
<!-- Cert image -->
<img class="card-img-top mb-3" src="cert_1.png" id="image-slider">
<!-- Cert image --> <img class="card-img-top mb-3" src="cert_1.png" id="image-slider">
</div>
</div>
</div>
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</div>
<div class="card-body d-flex flex-column">
<p>
<strong>Title</strong>: <a
href="https://doi.org/10.1073/pnas.79.8.2554">Neural networks and
physical systems with emergent collective computational abilities</a>
<strong>Title</strong>: <a href="https://doi.org/10.1073/pnas.79.8.2554">Neural networks and physical systems with emergent collective computational abilities</a>
</p>
<p>
<strong>Authors</strong>: J J Hopfield, <a
href="https://orcid.org/0000-0002-4344-2189">Wulfram Gerstner</a>,
Werner M. Kistler, <a
href="https://orcid.org/0000-0001-7383-3095">Richard Naud</a>, Liam
Paninski
<strong>Authors</strong>: J J Hopfield, <a href="https://orcid.org/0000-0002-4344-2189">Wulfram Gerstner</a>, Werner M. Kistler, <a href="https://orcid.org/0000-0001-7383-3095">Richard Naud</a>, Liam Paninski
</p>
<!-- Abstract section -->
<div id="abstract-section" class="scrollable-container">
<p>
<strong>Abstract</strong>: <i>Obtained from <a
href="https://www.crossref.org">CrossRef</a></i>
<strong>Abstract</strong>: <i>Obtained from <a href="https://www.crossref.org">CrossRef</a></i>
</p>
<div id="abstract-content" class="scrollable-text-box">
<p>
<jats:p>Computational properties of use of biological organisms or to
the construction of computers can emerge as collective properties of
systems having a large number of simple equivalent components (or
neurons). The physical meaning of content-addressable memory is
described by an appropriate phase space flow of the state of a system. A
model of such a system is given, based on aspects of neurobiology but
readily adapted to integrated circuits. The collective properties of
this model produce a content-addressable memory which correctly yields
an entire memory from any subpart of sufficient size. The algorithm for
the time evolution of the state of the system is based on asynchronous
parallel processing. Additional emergent collective properties include
some capacity for generalization, familiarity recognition,
categorization, error correction, and time sequence retention. The
collective properties are only weakly sensitive to details of the
modeling or the failure of individual devices.</jats:p>
<jats:p>Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.</jats:p>
</p>
</div>
</div>
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<strong>Certificate identifier</strong>: 2020-003
</p>
<p>
<strong>Codechecker name</strong>: <a
href="https://orcid.org/0000-0002-0024-5046">Daniel Nüst</a>
<strong>Codechecker name</strong>: <a href="https://orcid.org/0000-0002-0024-5046">Daniel Nüst</a>
</p>
<p>
<strong>Time of codecheck</strong>: 2020-04-06
</p>
<p>
<strong>Repository</strong>: <a
href="https://github.com/codecheckers/Hopfield-1982"
class="uri">https://github.com/codecheckers/Hopfield-1982</a>
<strong>Repository</strong>: <a href="https://github.com/codecheckers/Hopfield-1982" class="uri">https://github.com/codecheckers/Hopfield-1982</a>
</p>
<p>
<strong>Codecheck report</strong>: <a
href="https://doi.org/10.5281/zenodo.3741797"
class="uri">https://doi.org/10.5281/zenodo.3741797</a>
<strong>Codecheck report</strong>: <a href="https://doi.org/10.5281/zenodo.3741797" class="uri">https://doi.org/10.5281/zenodo.3741797</a>
</p>
<!-- Summary -->
<div id="summary-section" class="scrollable-container">
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});

</script>
</div>

<p style="margin-bottom: 2em;">
</p>
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